Predicting algal growth

under climate change in

the upper Thames

Mike Hutchins, CEH Wallingford

(plus Richard Williams, Christel

Prudhomme, Sue Crooks)

Changes in the Thames by 2080

• Brought about by economic and social

change but in particular climate change...

– Slower flowing

– Warmer

– Have more sunlight hours

– Have higher nutrient concentrations if only

due to less in stream dilution

• These are better environmental conditions

for phytoplankton blooms; and will favour

potentially-toxic Cyanobacteria species

... Defra policy interest

• How and when will climate change have a

discernible and significant impact on water

quality?

• Commissioning of a case study

demonstrating modelling tools and

datasets for assessing these changes:

three Lake District lakes

Yorkshire Ouse (focus on River Ure)

Upper River Thames

Model chain of three main components

• Climate data from Hadley Centre’s 11-member

ensemble projection (from regional climate

modelling (RCM)) used as part of the UKCP09

scenarios. The 11 members represent a range of

model parameterisations reflecting uncertainty. All

use the SRES A1B emission scenario.

• Future Flows Hydrology (FFH) dataset. Derived via

rainfall-runoff modelling under an EA project to

provide a UK-wide consistent set of future daily

river flows.

• Water quality predictions using QUESTOR, a semi-

empirical, process-based model of river networks

QUESTOR river quality model (Thames)

CEH weekly water quality (2009 - )

Upstream QUESTOR boundary

Tidal limit

Major urban areas outside London

LONDON

Model inputs: (1) Flow

and quality data in (a)

tributaries (b) effluents

from sewage works,

(2) Solar radiation

Represents biochemical interactions in

the river channel environment; and

energy balance for water temperature

Wallingford

Eynsham

Blooms likely in long slow-flowing rivers

...with sufficient light, nutrients and temperature to thrive. All

these variables used in hydrological modelling at daily resolution

of chlorophyll-a, and dissolved oxygen (DO) impacts.

Wallingford

(92 km downstream)

Effect of increasing residence time

Chlorophyll-a content of

different types of

phytoplankton is known,

making it a useful

surrogate for biomass

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up

pe

r q

uar

tile

ch

l-a

(µg

/L)

distance downstream (km)

River Thames (2009-10)

CEH Thames Initiative data

QUESTOR model

QUESTOR calibrated in 2009-10 (e.g. Eynsham)

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Jan-2009 Apr-2009 Jul-2009 Oct-2009 Jan-2010 Apr-2010 Jul-2010 Oct-2010 Jan-2011

Nit

roge

n: m

g N

O3-N

/L

Ph

osp

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rus:

µg

SR

P/L

-25-20-15-10-50510152025

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Wat

er

tem

p (

oC

)

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w (m

3s-1

)

Is model simulating physical/chemical parameters well?

For algae, good summer flow/temp simulation is critical

Temp

Flow

N

P

Simulated

Observed

Model performance at Abingdon in 2009

Phytoplankton

biomass (mg chl-a/L)

Limitation due to light

Nutrients are in excess

High flows wash phytoplankton out of system

An unexplained mid- to late- summer suppression of phytoplankton is apparent

1. However, large variations

observed between years. Far

more phytoplankton in 2009.

So a 2009-10 model is a

compromise

2. Best fitting year-specific

models perform much

better. They are identical,

apart from having different

grazing rates

Bar charts of

upper quartile

chl-a at Wallingford

Invasive zebra mussels are abundant in the Thames. We

assume that there are good and bad years for grazers but

we don’t know why? Over-winter flow/temperature

regimes. Interactions higher up food chain

Comparing 2009 & 2010: simulated blooms similar

Model evaluation and future priorities

• Environmental variables well represented. Can

identify suitable temperature-controlled growth rates

for a mixed phytoplankton population. All optimised

models have doubling rates of 48 h (+/- ~ 6 h)

• By altering year-specific death rates, model can

represent magnitudes of blooms year-on-year.

• Remaining gaps in understanding:

– controls on over-winter survival of phytoplankton grazers

– reasons for late-summer phytoplankton suppression

– water quality response to extreme events

– how will nutrient concentrations change in the future?

– what will be the impact of population growth, and changes

to management/treatment of water resources and waste?

What are impacts of flooding on water quality?

Wallingford – Dec 2012

July 2007 floods resulted in low DO (Oxford – Reading)

Many potential sources of uncertainty

Had-RM3 Perturbed Physics Ensemble Climate Model

Bias correction

Downscaling

Air temp PE

Rainfall-runoff Model

(CLASSIC, CERF)

Rainfall

Regression

Solar radiation

Attenuation by

trees and in

water column

Donate and scale

flows to unmodelled

tribuaries Pollutant

loads from

tributaries

(and STWs)

Photosynthetically

active radiation Water temp Flow

Water Quality Model (QUESTOR)

Phytoplankton biomass (chl-a) nutrients DO BOD

5

Key sources

to isolate

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1

3 2

Uncertainty due to hydrological modelling

I. A baseline QUESTOR model, set up using

all available flow data (2009-10)

II. Re-run QUESTOR replacing observed flows

in un-modelled tributaries with observed

flows donated and scaled from the modelled

tributaries.

III. Re-run again, also replacing observations

with modelled flows (where possible)

Only 5 of the 11 gauged tributaries were

modelled under the FFH project - so, 3 runs:

Errors due to donating (II) & modelling (III) flows

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Flow DO Temp

Run I

Run II

Run III

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Flow DO Temp

Run I

Run II

Run III

Eynsham Wallingford

Nash-Sutcliffe goodness-of-fit values (y-axis): impacts only small

How well is extreme water quality modelled using climate drivers?

DO BOD Temp Chl-a

Run I (2009-10) 0 0 0 27

30 year RCM/FFH 7.1-21.7 0.2-4.3 3.7-17.5 23.2-44.7

Run I (2009-10) 0 33 1.5 99.5

30 year RCM/FFH 1.7-9.2 13.0-36.9 1.7-17.4 75.9-103.0

Eynsham

Wallingford

• For RCM, 1961-90 is taken as a standard period indicative of

present day. RCM (and FFH) do not reproduce “real weather”.

• Days per year when undesirable thresholds exceeded (WFD-

relevant conditions: DO < 6 mg L-1, BOD > 4 mg L-1, Temp >

25 ºC, Chl-a > 0.03 mg L-1):

• When using climate model drivers the frequency of incidence

of extreme conditions is probably overestimated. Why?

Water quality is most vulnerable at low flows in summer

Flow Q95 (m3s-1) Eynsham Days Lock

1961-90 observed 1.17 3.36

2009-10 observed 1.44 4.02

Run I (2009-10) 2.12 4.11

Run II (2009-10) 0.68 3.06

Run III (2009-10) 1.33 3.47

30 year RCM/FFH 0.10-0.78 1.18-2.51

• Lowest flows are underestimated when using RCM/FFH

• Analysis of RCM outputs and climate records suggest

the highest air temperatures simulated by the models are

unfeasibly extreme.

• Climate model drivers suggest even in present day

conditions the Thames above Oxford is vulnerable to

drying out. This is not realistic.

Summary results

1. The increase

represents the future

2040-69 situation

relative to present day.

2. The bar represents

the mean of changes

seen from the 11

applications of the

model chain

3. Error bars represent

the maximum and

minimum change.

• Changes in drivers by the 2040-69 period (Wallingford): + 3-5 ºC 90th percentile (i.e. summer) air temperature

+ 4-10% solar radiation (70th percentile)

- 25% Q95 flow i.e. summer low flow (range: +7.3 to -41.3)

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DO BOD Temp chl-a

Incr

ease

in d

ays

per

yea

r

Wallingford

-5

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DO BOD Temp chl-a

Incr

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yea

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Eynsham

Threshold values:DO = 6 mg/LBOD = 4 mg/LTemp = 25 Cchl-a = 0.03 mg/L

• Whilst simulations derived from RCM applications

appear reliable across the inter-quartile range (and to a

large degree to 5th and 95th percentile levels), the most

extreme conditions are not simulated reliably.

• The future projections should not be presented as

absolute indicators of water quality, rather as a change

relative to present day conditions.

• Accelerated phytoplankton growth in future will lead to

more limitation (including self-shading) and greater risk

of blooms crashing, leading to possible DO sags.

• Uncertainty in model chain:

Conclusions

Climate

modelling

Water quality

modelling

Hydrological

modelling